The Use of Machine Learning to Support the Diagnosis of Oral Alterations
Keywords:
Deep Learning, Mouth Neoplasms, Neural Networks, Computer, Machine LearningAbstract
Objective: To verify the accuracy of deep learning models in detecting cellular alterations in histological images of oral mucosa. Material and Methods: The study compares three convolutional neural network (CNN) architectures for classifying histological images: EfficientNet-B3, MobileNet-V2, and VGG16. Efficient and focused on computer vision, each has specific advantages. A Kaggle database with 5192 images was used, divided into training (70%), validation (15%), and test (15%) sets. The CNNs were implemented using the Keras library, trained with pre-trained ImageNet weights, and evaluated using accuracy and AUC metrics. Results: The findings indicate that EfficientNet-B3 achieved the lowest training and validation losses at epoch 30, with the highest accuracy and stability during training. Evaluation metrics showed EfficientNet-B3 with 98% accuracy and 99% sensitivity for oral squamous cell carcinoma (OSCC) images, outperforming MobileNet-V2 and VGG16. MobileNet-V2 achieved 97% accuracy and 96% sensitivity, while VGG16 reached 94% accuracy and 93% sensitivity for OSCC images. All models exhibited high sensitivity and specificity in differentiating between normal and OSCC images, as demonstrated by ROC curves. EfficientNet-B3 had the highest AUC (0.982), followed by MobileNet-V2 (AUC=0.967) and VGG16 (AUC=0.937). These findings underscore the effectiveness of EfficientNet-B3 for accurately detecting cellular alterations in histological images of oral mucosa. Conclusion: Our study reveals the superior performance of CNNs, particularly EfficientNet-B3, in classifying histological images of OSCC.
References
Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin DM, Piñeros M, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 2019; 144(8):1941-1953. https://doi.org/10.1002/ijc.31937
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN Estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71(3):209-249. https://doi.org/10.3322/caac.21660
Warnakulasuriya S. Global epidemiology of oral and oropharyngeal cancer. Oral Oncol 2009; 45(4-5):309-316. https://doi.org/10.1016/j.oraloncology.2008.06.002
Mignogna MD, Fedele S, Russo L Lo. The world cancer report and the burden of oral cancer. Eur J Cancer Prev 2004; 13(2):139-142. https://doi.org/10.1097/00008469-200404000-00008
Johnson NW, Warnakulasuriya S, Gupta PC, Dimba E, Chindia M, Otoh EC, et al. Global oral health inequalities in incidence and outcomes for oral cancer. Adv Dent Res 2011; 23(2):237-246. https://doi.org/10.1177/0022034511402082
Boing AF, Antunes JLF, Carvalho MB, Gois Filho JF, Kowalski LP, Michaluart P, et al. How much do smoking and alcohol consumption explain socioeconomic inequalities in head and neck cancer risk? J Epidemiol Community Heal 2011; 65(8):709-714. https://doi.org/10.1136/jech.2009.097691
Huang S, O Sullivan B. Oral cancer: Current role of radiotherapy and chemotherapy. Med Oral Patol Oral y Cir Bucal 2013; 18(2):e233-240. https://doi.org/10.4317/medoral.18772
Johnson NW, Jayasekara P, Amarasinghe AA, Hemantha K. Squamous cell carcinoma and precursor lesions of the oral cavity: Epidemiology and aetiology. Periodontol 2000 2011; 57(1):19-37. https://doi.org/10.1111/j.1600-0757.2011.00401.x
Chaurasia A, Alam S, Singh N. Oral cancer diagnostics: An overview. Natl J Maxillofac Surg 2021; 12(3):324-332. https://doi.org/10.4103/njms.NJMS_130_20
Mehlum CS, Larsen SR, Kiss K, Groentved AM, Kjaergaard T, Möller S, et al. Laryngeal precursor lesions: Interrater and intrarater reliability of histopathological assessment. Laryngoscope 2018; 128(10):2375-2379. https://doi.org/10.1002/lary.27228
Adeoye J, Tan JY, Choi S-W, Thomson P. Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review. Int J Med Inform 2021; 154:104557. https://doi.org/10.1016/j.ijmedinf.2021.104557
Chu CS, Lee NP, Adeoye J, Thomson P, Choi S. Machine learning and treatment outcome prediction for oral cancer. J Oral Pathol Med 2020; 49(10):977-985. https://doi.org/10.1111/jop.13089
Wang X, Li B. Deep learning in head and neck tumor multiomics diagnosis and analysis: Review of the literature. Front Genet 2021; 12:624820. https://doi.org/10.3389/fgene.2021.624820
Yang SY, Li SH, Liu JL, Sun XQ, Cen YY, Ren RY, et al. Histopathology-based diagnosis of oral squamous cell carcinoma using deep learning. J Dent Res 2022; 101(11):1321-1327. https://doi.org/10.1177/002203452210898
Liu W, Yuan X, Guo L, Pan F, Wu C, Sun Z, et al. Artificial intelligence for detecting and delineating margins of early ESCC under WLI endoscopy. Clin Transl Gastroenterol 2022; 13(1):e00433. https://doi.org/10.14309/ctg.0000000000000433
Soni A, Sethy PK, Dewangan AK, Nanthaamornphong A, Behera SK, Devi B. Enhancing oral squamous cell carcinoma detection: A novel approach using improved EfficientNet architecture. BMC Oral Health 2024; 24(1):601. https://doi.org/10.1186/s12903-024-04307-5
Albuquerque R, Rodrigues A, Ferrucio G, Aguiar J, Filho JA, Madeiro F. Efficientnets aplicadas à esteganálise em imagens digitais. Rev Engen Pesqui Apli 2022; 7(2):32-41. https://doi.org/10.25286/repa.v7i2.2215 [In Portuguese].
Tan M, Le Q V. EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, ICML 2019; 6105-6114. https://doi.org/10.48550/arXiv.1905.11946
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. MobileNetV2: Inverted residuals and linear bottlenecks.: Proceedings of the IEEE conference on computer vision and pattern recognition 2018; 4510-4520. https://doi.org/10.1109/CVPR.2018.00474
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 3rd Int Conf Learn Represent (ICLR 2015). Computational and Biological Learning Society 2015; 1–14. https://doi.org/10.48550/arXiv.1409.1556
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res 2011; 12:2825-2830. https://doi.org/10.48550/arXiv.1201.0490
Goyal P, Dollár P, Girshick R, Noordhuis P, Wesolowski L, Kyrola A, et al. Accurate, large minibatch SGD: Training ImageNet in 1 hour. J CoRR 2017; 1-12. https://doi.org/10.48550/arXiv.1706.02677
Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database. 2009 IEEE Conf Computer Vision and Pattern Recognition 2009; 248-255. https://doi.org/10.1109/CVPR.2009.5206848
Sultan AS, Elgharib MA, Tavares T, Jessri M, Basile JR. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. J Oral Pathol Med 2020; 49(9):849-856. https://doi.org/10.1111/jop.13042
Mahmood H, Shaban M, Indave BI, Santos-Silva AR, Rajpoot N, Khurram SA. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review. Oral Oncol 2020; 110:104885. https://doi.org/10.1016/j.oraloncology.2020.104885
Xu Z, Peng J, Zeng X, Xu H, Chen Q. High-accuracy oral squamous cell carcinoma auxiliary diagnosis system based on EfficientNet. Front Oncol 2022; 12:894978. https://doi.org/10.3389/fonc.2022.894978
Jubair F, Al-karadsheh O, Malamos D, Al Mahdi S, Saad Y, Hassona Y. A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis 2022; 28(4):1123-1130. https://doi.org/10.1111/odi.13825
Albalawi E, Thakur A, Ramakrishna MT, Bhatia Khan S, SankaraNarayanan S, Almarri B, et al. Oral squamous cell carcinoma detection using EfficientNet on histopathological images. Front Med 2024; 10:1349336. https://doi.org/10.3389/fmed.2023.1349336
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Pesquisa Brasileira em Odontopediatria e Clínica Integrada
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
